In [384]:
# https://towardsdatascience.com/getting-more-value-from-the-pandas-value-counts-aa17230907a6
# https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/
# https://effectiveml.com/using-grid-search-to-optimise-catboost-parameters.html
# https://machinelearningmastery.com/smote-oversampling-for-imbalanced-classification/
# https://www.kaggle.com/c/LANL-Earthquake-Prediction/discussion/89909

Capstone Project: Create a Customer Segmentation Report for Arvato Financial Services

In this project, you will analyze demographics data for customers of a mail-order sales company in Germany, comparing it against demographics information for the general population. You'll use unsupervised learning techniques to perform customer segmentation, identifying the parts of the population that best describe the core customer base of the company. Then, you'll apply what you've learned on a third dataset with demographics information for targets of a marketing campaign for the company, and use a model to predict which individuals are most likely to convert into becoming customers for the company. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.

If you completed the first term of this program, you will be familiar with the first part of this project, from the unsupervised learning project. The versions of those two datasets used in this project will include many more features and has not been pre-cleaned. You are also free to choose whatever approach you'd like to analyzing the data rather than follow pre-determined steps. In your work on this project, make sure that you carefully document your steps and decisions, since your main deliverable for this project will be a blog post reporting your findings.

In [413]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
import operator

from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier as xgb
import catboost as cb

from imblearn.over_sampling import SMOTE
from collections import Counter
from sklearn.cluster import KMeans
from sklearn.model_selection import GridSearchCV

pd.options.mode.chained_assignment = None  # default='warn'

from IPython.display import Image

# magic word for producing visualizations in notebook
%matplotlib inline
In [2]:
# show maximum number of columns
pd.set_option('display.max_columns', None)
pd.set_option('max_colwidth', None)

Part 0: Get to Know the Data

There are four data files associated with this project:

  • Udacity_AZDIAS_052018.csv: Demographics data for the general population of Germany; 891 211 persons (rows) x 366 features (columns).
  • Udacity_CUSTOMERS_052018.csv: Demographics data for customers of a mail-order company; 191 652 persons (rows) x 369 features (columns).
  • Udacity_MAILOUT_052018_TRAIN.csv: Demographics data for individuals who were targets of a marketing campaign; 42 982 persons (rows) x 367 (columns).
  • Udacity_MAILOUT_052018_TEST.csv: Demographics data for individuals who were targets of a marketing campaign; 42 833 persons (rows) x 366 (columns).

Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. Use the information from the first two files to figure out how customers ("CUSTOMERS") are similar to or differ from the general population at large ("AZDIAS"), then use your analysis to make predictions on the other two files ("MAILOUT"), predicting which recipients are most likely to become a customer for the mail-order company.

The "CUSTOMERS" file contains three extra columns ('CUSTOMER_GROUP', 'ONLINE_PURCHASE', and 'PRODUCT_GROUP'), which provide broad information about the customers depicted in the file. The original "MAILOUT" file included one additional column, "RESPONSE", which indicated whether or not each recipient became a customer of the company. For the "TRAIN" subset, this column has been retained, but in the "TEST" subset it has been removed; it is against that withheld column that your final predictions will be assessed in the Kaggle competition.

Otherwise, all of the remaining columns are the same between the three data files. For more information about the columns depicted in the files, you can refer to two Excel spreadsheets provided in the workspace. One of them is a top-level list of attributes and descriptions, organized by informational category. The other is a detailed mapping of data values for each feature in alphabetical order.

In the below cell, we've provided some initial code to load in the first two datasets. Note for all of the .csv data files in this project that they're semicolon (;) delimited, so an additional argument in the read_csv() call has been included to read in the data properly. Also, considering the size of the datasets, it may take some time for them to load completely.

You'll notice when the data is loaded in that a warning message will immediately pop up. Before you really start digging into the modeling and analysis, you're going to need to perform some cleaning. Take some time to browse the structure of the data and look over the informational spreadsheets to understand the data values. Make some decisions on which features to keep, which features to drop, and if any revisions need to be made on data formats. It'll be a good idea to create a function with pre-processing steps, since you'll need to clean all of the datasets before you work with them.

0.1. Load data
In [3]:
# load in the data
azdias_read = pd.read_csv('Udacity_AZDIAS_052018.csv', sep=',')
customers_read = pd.read_csv('Udacity_CUSTOMERS_052018.csv', sep=',')

C:\Users\Ieva\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3058: DtypeWarning: Columns (19,20) have mixed types.Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

Loaded csv files to dataframes. There are some warning which inform that some columns have mixed data types. So, I will have to keep in mind that cases.

There is a column Unnamed: 0 in both dataframes. I removed this column as it shows the index of the dataframe and does not bring any additional information.

In [4]:
# drop index column
azdias = azdias_read.drop(['Unnamed: 0'], axis = 1)
customers = customers_read.drop(['Unnamed: 0'], axis = 1)
0.2 Combine attributes with all possible values.

Load description of features to test if all features satisfies the requirements provided in the excel sheet. The goal is to find out if there are any additional values in customers dataframe which can not be found in the attributes descriptions.

In [5]:
attributes = pd.read_excel('DIAS Attributes - Values 2017.xlsx', header = 0, names = ['unknown', 'Attribute', 'Description', 'Value', 'meaning']).drop(['unknown', 'meaning'], axis = 1).fillna(method='ffill')
attributes = attributes.drop(attributes.index[0])

Originally, there i a file "DIAS Attributes - Values 2017.xlsx" where you can find the attribute and the description of the column. I aggregated all the possible values for each column to combine everything to array.

In [6]:
# Excluded attributes where value can get many different numerical value. 
attributes_values = attributes[attributes['Value'] != "…"].groupby('Attribute')['Value'].apply(list)
attributes_values_df = pd.DataFrame({'attribute':attributes_values.index, 'attribute_values': attributes_values.values})

Aggregate dataframes values and save possible values to the new dataframe

In [7]:
def collect_column_values(df):
    """Returns dataframe with its columns and possible values

    Parameters:
        df (dataframe): Original dataframe;

    Returns:
        dataframe: column and values
    """
    temp_list = []
    for col in df.columns:
        val = list(df.loc[:, col].unique())
        temp_list.append((col, val))
    result = pd.DataFrame(temp_list, columns = ['column', 'values'])
    return result
In [8]:
customers_values = collect_column_values(customers)
In [9]:
attr_comparison_df = pd.merge(customers_values, attributes_values_df,  how='left', left_on=['column'], right_on = ['attribute'])
In [10]:
attr_comparison_df[attr_comparison_df['attribute_values'].notnull()].count()
Out[10]:
column              265
values              265
attribute           265
attribute_values    265
dtype: int64

Only 265 out of 370 columns in the customers dataframe have columns which descriptions we can find in the attributes excel sheet.

In [24]:
attribute_differences = []

attr_comparison_df = attr_comparison_df[attr_comparison_df['attribute_values'].notnull()]
for index, row in attr_comparison_df.iterrows():
    attribute_differences.append((attr_comparison_df['column'][index], set(attr_comparison_df['values'][index]) - set(attr_comparison_df['attribute_values'][index])))
In [26]:
attribute_differences_df = pd.DataFrame(attribute_differences, columns = ['column', 'values'])
attribute_differences_df['length'] = attribute_differences_df['values'].str.len()
attribute_differences_df.sort_values('length', ascending = False, inplace = True)
In [29]:
# I filter columns where difference between original value and documentation differ at least one value. 
attribute_differences_df[attribute_differences_df['length'] > 1].head(60)
Out[29]:
column values length
4 CAMEO_DEUG_2015 {nan, 3, 7, 5, 9, X, 1, 8, 4, 2, 6} 11
74 KBA05_KRSHERST3 {nan, 9.0} 2
68 KBA05_HERST4 {nan, 9.0} 2
79 KBA05_KW1 {nan, 9.0} 2
78 KBA05_KRSZUL {nan, 9.0} 2
77 KBA05_KRSVAN {nan, 9.0} 2
76 KBA05_KRSOBER {nan, 9.0} 2
75 KBA05_KRSKLEIN {nan, 9.0} 2
109 KBA05_ZUL2 {nan, 9.0} 2
73 KBA05_KRSHERST2 {nan, 9.0} 2
72 KBA05_KRSHERST1 {nan, 9.0} 2
71 KBA05_KRSAQUOT {nan, 9.0} 2
70 KBA05_HERSTTEMP {nan, 9.0} 2
69 KBA05_HERST5 {nan, 9.0} 2
67 KBA05_HERST3 {nan, 9.0} 2
81 KBA05_KW3 {nan, 9.0} 2
66 KBA05_HERST2 {nan, 9.0} 2
65 KBA05_HERST1 {nan, 9.0} 2
64 KBA05_GBZ {nan, 0.0} 2
63 KBA05_FRAU {nan, 9.0} 2
62 KBA05_DIESEL {nan, 9.0} 2
61 KBA05_CCM4 {nan, 9.0} 2
60 KBA05_CCM3 {nan, 9.0} 2
59 KBA05_CCM2 {nan, 9.0} 2
58 KBA05_CCM1 {nan, 9.0} 2
57 KBA05_BAUMAX {0.0, nan} 2
80 KBA05_KW2 {nan, 9.0} 2
82 KBA05_MAXAH {nan, 9.0} 2
110 KBA05_ZUL3 {nan, 9.0} 2
83 KBA05_MAXBJ {nan, 9.0} 2
106 KBA05_VORB1 {nan, 9.0} 2
105 KBA05_VORB0 {nan, 9.0} 2
104 KBA05_SEG9 {nan, 9.0} 2
103 KBA05_SEG8 {nan, 9.0} 2
102 KBA05_SEG7 {nan, 9.0} 2
101 KBA05_SEG6 {nan, 9.0} 2
100 KBA05_SEG5 {nan, 9.0} 2
99 KBA05_SEG4 {nan, 9.0} 2
98 KBA05_SEG3 {nan, 9.0} 2
97 KBA05_SEG2 {nan, 9.0} 2
96 KBA05_SEG10 {nan, 9.0} 2
95 KBA05_SEG1 {nan, 9.0} 2
94 KBA05_MOTRAD {nan, 9.0} 2
93 KBA05_MOTOR {nan, 9.0} 2
92 KBA05_MODTEMP {nan, 6.0} 2
91 KBA05_MOD8 {nan, 9.0} 2
90 KBA05_MOD4 {nan, 9.0} 2
89 KBA05_MOD3 {nan, 9.0} 2
88 KBA05_MOD2 {nan, 9.0} 2
87 KBA05_MOD1 {nan, 9.0} 2
86 KBA05_MAXVORB {nan, 9.0} 2
85 KBA05_MAXSEG {nan, 9.0} 2
84 KBA05_MAXHERST {nan, 9.0} 2
56 KBA05_AUTOQUOT {nan, 9.0} 2
111 KBA05_ZUL4 {nan, 9.0} 2
108 KBA05_ZUL1 {nan, 9.0} 2
239 REGIOTYP {nan, 0.0} 2
3 CAMEO_DEU_2015 {nan, XX} 2
259 W_KEIT_KIND_HH {0.0, nan} 2
223 LP_LEBENSPHASE_GROB {nan, 0.0} 2

For all the columns which names could be found in attribute_differences_df, I looked through these values to and compared possible values and the actual values. For instance, CAMEO_DEUG_2015 has 11 values, while value XX is not defined. Then I replace that values with NaN.

0.3 Mixed type columns

While reading files, I recognized that there are warning because of mixed data types

In [11]:
def find_mixed_type_columns(df):
    """Returns list with column names which have multiple types.

    Parameters:
        df (dataframe): Original dataframe
        
    Returns:
        list: with mixed data type
    """
    cols = []
    for col in df.columns:
        mixed = (df[[col]].applymap(type) != df[[col]].iloc[0].apply(type)).any(axis = 1)
        if len(df[mixed]) > 0:
            cols.append(col)
    return cols
In [12]:
def unify_mixed_column_types(df, cols):
    """Converts column values to string and splits to . character

    Parameters:
        df (dataframe): Original dataframe
        cols (list): column names with mixed data types
        
    Returns:
        dataframe with unified columns values
    """
    for col in cols:
        df[col] = df[col].astype(object)
        df[col] = df[col].str.split('.', expand = True)[0]
    return df

Foundthat thera are 5 columns where types are different: string and double. 'CAMEO_DEU_2015', 'CAMEO_DEUG_2015', 'CAMEO_INTL_2015','D19_LETZTER_KAUF_BRANCHE','EINGEFUEGT_AM', 'OST_WEST_KZ'

In these datasets the majority of columnsare float type 267, integers 93, objects 6.

In [13]:
azdias.dtypes.value_counts()
Out[13]:
float64    267
int64       93
object       6
dtype: int64
In [14]:
customers.dtypes.value_counts()
Out[14]:
float64    267
int64       94
object       8
dtype: int64
In [16]:
def cols_types(df):
    """ split dataframe columns by type

    Parameters:
        df (dataframe): Original dataframe
        
    Returns:
        list categorical and numerical features column names lists
    """
    cols = df.dtypes
    categorical_cols = [key for key in dict(cols)
             if dict(df.dtypes)[key] in ['object']]
    numerical_cols = [key for key in dict(cols)
             if dict(df.dtypes)[key] in ['float64','float32','int32','int64']]
    return categorical_cols, numerical_cols
In [17]:
categorical_customers, numerical_customers = cols_types(customers)
categorical_azdias, numerical_azdias = cols_types(azdias)
0.4. High cardinality columns

The goal is to identify categorical columns which have more than 2 unique values

In [391]:
def get_high_cardinality_columns(df, cat_cols, limit):
    """Splits categorical columns by number of unique values

    Parameters:
        df (dataframe): Original dataframe
        cat_cols (list): categorical columns name lists
        limit (integer): number of categories
        
    Returns:
        list: non_binary_cat - columns with more categories than limit; 
        binary_cat - columns with 2 categories
    """
    for col in cat_cols:
        if len(df[col].unique()) > limit:
            non_binary_cat.append(col)
        elif len(df[col].unique()) == 2:
            binary_cat.append(col)
    return non_binary_cat, binary_cat
In [20]:
non_binary_customers, binary_customers = get_high_cardinality_columns(customers, categorical_customers, 10)
In [21]:
non_binary_customers
Out[21]:
['CAMEO_DEU_2015',
 'CAMEO_DEUG_2015',
 'CAMEO_INTL_2015',
 'D19_LETZTER_KAUF_BRANCHE',
 'EINGEFUEGT_AM']

As a result of high cardinality, I created functions (maps) to reduce the number of categories. For example customers["CAMEO_INTL_2015"] had values 14.0 (double) and 14 (string).

In [22]:
def replace_cameo_deug_values(x):
    """replace values based on conditions

    Parameters:
        df (int): original column value
        
    Returns:
        changed column values based on rules
    """
    if x in (-1, 5):
        return x
    if x in (1, 2):
        return 1
    if x in (3, 4):
        return 2
    if x in (6,7):
        return 3
    if x in (8, 9):
        return 4
In [31]:
def replace_values(df):
    """Returns dataframe with modified values because of missing description.

    Parameters:
        df (dataframe): Original dataframe
        
    Returns:
        dataframe: dataframe with set selected columns to None
    """
    df_new = df
    df_new['KBA05_MODTEMP'] = df['KBA05_MODTEMP'].replace(6, None)
    df_new['CAMEO_DEU_2015'] = df['CAMEO_DEU_2015'].replace("XX", None)
    df_new['LP_LEBENSPHASE_GROB'] = df['LP_LEBENSPHASE_GROB'].replace(0, None)
    df_new['CAMEO_DEUG_2015'] = df['CAMEO_DEUG_2015'].replace("X", None)
    df_new['CAMEO_INTL_2015'] = df['CAMEO_INTL_2015'].replace("XX", None)

    return df_new
  1. Not all columns has descriptios in files:./DIAS Information Levels - Attributes 2017.xlsx, because shape of populiation (azdias df is 367) while in the file 314.
  2. Calculate the percent of null information in the df. There are some columns where missing values > 90%. These columns should be excluded as to not get confusion.
0.5 Exclude columns based on missing values share

There are a lot of columns where missing data has a higher share than known data. For example, ALTER_KIND4 99.86% of missing values

In [34]:
(azdias.isnull().sum()/len(azdias)*100).sort_values(ascending = False)
Out[34]:
ALTER_KIND4               99.864792
ALTER_KIND3               99.307691
ALTER_KIND2               96.690047
ALTER_KIND1               90.904837
EXTSEL992                 73.399639
                            ...    
D19_VERSI_ONLINE_DATUM     0.000000
D19_VERSICHERUNGEN         0.000000
D19_VOLLSORTIMENT          0.000000
D19_WEIN_FEINKOST          0.000000
LNR                        0.000000
Length: 366, dtype: float64

We can define in advance when to drop columns based on missing data share.

In [35]:
def exclude_columns_with_many_missing_data(df, missing_ratio):
    """Returns columns list where missing values exceeds defined missing_ratio.

    Parameters:
        df (dataframe): Original dataframe;
        missing_ration (int): defines the ratio of accepted missingness

    Returns:
        list: df column names which should be exluded
    """
    columns = list(df.columns[df.isnull().mean() >= missing_ratio])
    return columns

Investigate which columns have more than defined missing values. I chose statistical point of view and selected to exclude values where missing values is higher than the first quartile (>=25%)

In [36]:
customers_missing_columns = exclude_columns_with_many_missing_data(customers, 0.25)
azdias_missing_columns = exclude_columns_with_many_missing_data(azdias, 0.25)

columns_to_exclude = customers_missing_columns + list(set(azdias_missing_columns) - set(customers_missing_columns))

# Finally, I checked if azdias and customers dataframes have the same columns with set(azdias.columns) - set(customers.columns)
# Because both dataframes have the same columns, I chose one of the dataframes columns and removed columns (columns_to_exclude).
columns_to_keep = set(azdias.columns) - set(columns_to_exclude)
0.6 Calculate correlations

Because there are a lot of columns, it would be quite hard to check all columns relationship manually. As a result, I selected to write correlation function. Also, dropped columns where correlation coefficient exceeds defined limit

In [37]:
def calculate_correlations(df, cor_threshold):
    """Calculate correlation and return highly correlated columns list

    Parameters:
        df (dataframe): Original dataframe;
        cor_threshold (int): filter correlation matrix and select columns with higher correlation than threshold

    Returns:
        list: highly correlated columns list
    """
    corr_matrix = df.corr().abs() 
    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k = 1).astype(np.bool))
    to_drop = [column for column in upper.columns if any(upper[column] > cor_threshold)]
    
    return to_drop

Remove columns from dataframe when given list of columns

In [38]:
def remove_columns(df, columns_list_to_remove):
    """Returns dataframe without columns from provided list. 

    Parameters:
        df (dataframe): Original dataframe;
        df (list): column lists to exclude

    Returns:
        dataframe: without defined columns
    """  
    return df[[col for col in df.columns if col not in columns_list_to_remove]]
0.6. Impute missing data

Even removed columns with missing data where missing data share exceeds limit, I selected to use imputer. There I selected to use median strategy, as it would be more statistically correct if data has a lot of outliers.

In [39]:
def impute_missing_data(df):
    """Impute missing data with median value. 

    Parameters:
        df (dataframe): original dataframe;

    Returns:
        dataframe: with filled missing values
    """  
    imputer = SimpleImputer(missing_values = np.nan, strategy = 'median')
    imputer = imputer.fit(df)
    return imputer.transform(df)
In [452]:
def add_columns(cols_to_keep, v):
    """Select columns based on function parameters 

    Parameters:
        cols_to_keep (list): columns list to keep;
        v (boolean): logical value to check if value was used in other function

    Returns:
        list: columns to keep
    """  
    if v == False:
        res = cols_to_keep
    elif "RESPONSE" in cols_to_keep:
        res = cols_to_keep.remove("RESPONSE")
    else:
        res = cols_to_keep
    return res
0.7. Final data cleaning function
In [311]:
def prepare_data (df1, df2, missing_percent, cor_threshold, val = True):
    """Returns prepared dataframe

    Parameters:
        df1 (dataframe): Original dataframe;
        df2 (dataframe): Other dataframe;
        missing_percent (double): maximum value to exclude columns with missing data;
        cor_threshold (double): maximum value to eclude columns with high correlations

    Returns:
        dataframe: cleaned dataframe
    """         
    common_cols = list(set(df1.columns) & set(df2.columns))
    
    df1_new = replace_values(df1[common_cols])
    print("replace unknown column values")
    
    mixed_cols = find_mixed_type_columns(df1)
    print("find mixed column types")
    
    df1_new = unify_mixed_column_types(df1_new, mixed_cols)
    print("replace mixed columns to single type")
    
    df1_new["CAMEO_INTL_2015"] = np.where(df1_new.CAMEO_INTL_2015.isnull(),
         np.nan,
         df1_new.CAMEO_INTL_2015.str.slice(0,1))
    
    df1_new['CAMEO_DEUG_2015'] = df1_new['CAMEO_DEUG_2015'].apply(replace_cameo_deug_values)

    df1_missing_columns = exclude_columns_with_many_missing_data(df1, missing_percent)
    df2_missing_columns = exclude_columns_with_many_missing_data(df2, missing_percent)
    print("removed column with missing data with more than {0} percent".format(missing_percent))
    
    if val == True:
        columns_to_exclude = df1_missing_columns + list(set(df2_missing_columns) - set(df1_missing_columns))
    else:
        columns_to_exclude = df1_missing_columns
    print("collect missing columns names")
    
    columns_to_keep = set(df1.columns) - set(columns_to_exclude) - set(['PRODUCT_GROUP', 'CUSTOMER_GROUP', 'ONLINE_PURCHASE'])
    print("keep relevant columns")
    
    columns_to_keep = add_columns(columns_to_keep, val)
    
    df1_new = df1_new[columns_to_keep]
        
    df1_cols = calculate_correlations(df1_new, cor_threshold)
    res = remove_columns(df1_new, df1_cols)
    
    return res
In [390]:
customers_upd_df = prepare_data(customers, azdias, 0.25, 0.8)

replace unknown column values
find mixed column types
replace mixed columns to single type
removed column with missing data with more than 0.25 percent
collect missing columns names
keep relevant columns
In [42]:
azdias_upd_df = prepare_data(azdias, customers, 0.25, 0.8)

replace unknown column values
find mixed column types
replace mixed columns to single type
removed column with missing data with more than 0.25 percent
collect missing columns names
keep relevant columns
In [236]:
# customers dataframe shape after modifications
customers_upd_df.shape
Out[236]:
(191652, 100)
In [44]:
# azdias dataframe shape after modifications
azdias_upd_df.shape
Out[44]:
(891221, 105)
In [478]:
sns.heatmap(customers.corr(), vmin = -1, vmax = 1, cmap = "RdBu")
Out[478]:
<matplotlib.axes._subplots.AxesSubplot at 0x1984920d6c8>
In [45]:
# overall correlation matrix of customers df
sns.heatmap(customers_upd_df.corr(), vmin = -1, vmax = 1, cmap = "RdBu")
Out[45]:
<matplotlib.axes._subplots.AxesSubplot at 0x197933b3e88>
In [46]:
# overall correlation matrix of population df
sns.heatmap(azdias_upd_df.corr(), vmin = -1, vmax = 1, cmap = "RdBu")
Out[46]:
<matplotlib.axes._subplots.AxesSubplot at 0x1979232f0c8>

As the number of the dataframes shapes differ, I am going to select common cols for both datasets

In [47]:
common_cols = set(customers_upd_df.columns) & set(azdias_upd_df.columns)
In [48]:
customers_upd_df = customers_upd_df[common_cols]
azdias_upd_df = azdias_upd_df[common_cols]
In [49]:
(azdias_upd_df.isnull().sum()/len(azdias_upd_df)*100).sort_values(ascending = False)
Out[49]:
VK_DISTANZ               8.518314
KONSUMNAEHE              8.299737
ANZ_TITEL                8.247000
WOHNDAUER_2008           8.247000
EINGEZOGENAM_HH_JAHR     8.247000
                           ...   
D19_GARTEN               0.000000
PRAEGENDE_JUGENDJAHRE    0.000000
D19_DROGERIEARTIKEL      0.000000
D19_VERSAND_REST         0.000000
SEMIO_KAEM               0.000000
Length: 96, dtype: float64

Even after data cleaning function, there are some columns with small share of data. I am going to show the share of missing values

In [251]:
def plot_missing_values_share(df, limit):
    """Returns barplot with missing data columns, ordered descending by the biggest missing values count

    Parameters:
        df (dataframe): Original dataframe;
        limit (integer): selected value to show in the bar chart;

    Returns:
        visualization
    """         
    df_calc = ((df.isnull().sum()/len(df)*100).sort_values(ascending = False)).head(limit)
    sns.barplot(df_calc.index, df_calc.values, palette = "muted").set_title('column names with missing values')
    plt.xticks(rotation=70)
In [252]:
plot_missing_values_share(azdias, 10)
In [454]:
plot_missing_values_share(azdias_upd_df, 10)

What I did for data preprocessing:

  1. checked type of columns and identified mixed type columns
  2. found column values which were not decribed in the columns explanation file.
  3. Assign null/nan values to columns if these values are incorrect
  4. mixed type columns changed to single type
  5. replace column values with defined rules
  6. excluded columns with many missing data
  7. calculated correlation and removed highly correlated values
  8. evaluated high cardinality categorical columns

Part 1: Customer Segmentation Report

The main bulk of your analysis will come in this part of the project. Here, you should use unsupervised learning techniques to describe the relationship between the demographics of the company's existing customers and the general population of Germany. By the end of this part, you should be able to describe parts of the general population that are more likely to be part of the mail-order company's main customer base, and which parts of the general population are less so.

Because there are many columns, it is important to reduce the dimension. For this I selected PCA. For PCA application it is important to scale variables. For this I selected standard scaler.

1.1 standartization
In [53]:
scaler = StandardScaler()
In [54]:
def scale_df(df):
    """Scale dataframe values. 

    Parameters:
        df (dataframe): Original dataframe;

    Returns:
        dataframe: scaled dataframe values
    """  
    df_scaled = scaler.fit(df).transform(df)
    return df_scaled
In [55]:
customers_scaled = scale_df(impute_missing_data(customers_upd_df))
azdias_scaled = scale_df(impute_missing_data(azdias_upd_df))

Firstly, to determine the number of components, I plotted screeplot. Screeplot shows how many components I have to choose to explain some percent of data. In literature, it is recommended to select number of components when explained variance is over 80%. In this case I selected when cumulative variance covers 85%.

1.2. PCA application
In [56]:
pca = PCA().fit(azdias_scaled)
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('number of components')
plt.ylabel('cumulative explained variance');

In this case, I need 56 components

In [57]:
np.cumsum(pca.explained_variance_ratio_)
Out[57]:
array([0.12386682, 0.22617455, 0.27186916, 0.30538017, 0.33268448,
       0.35824487, 0.37789598, 0.3951579 , 0.41197198, 0.42842628,
       0.44390736, 0.45813626, 0.47152007, 0.48432656, 0.49671555,
       0.5087414 , 0.52066476, 0.53210565, 0.54318799, 0.55384604,
       0.5642941 , 0.57470406, 0.58491536, 0.59482175, 0.60466311,
       0.61432298, 0.62395571, 0.63345899, 0.64291806, 0.65202279,
       0.6610571 , 0.66983353, 0.67846229, 0.68702278, 0.69551583,
       0.70392121, 0.71208353, 0.72022682, 0.72818727, 0.7360954 ,
       0.74388152, 0.75157755, 0.75918016, 0.76674302, 0.77424417,
       0.78172932, 0.78910967, 0.79640496, 0.80367055, 0.81074052,
       0.81775407, 0.82474833, 0.83162499, 0.8383756 , 0.84497356,
       0.85151277, 0.85802113, 0.86445332, 0.87074921, 0.87692828,
       0.88292744, 0.88875678, 0.89455725, 0.90017709, 0.90575948,
       0.91119431, 0.91652885, 0.92180716, 0.92681142, 0.93174264,
       0.93638067, 0.94075366, 0.94494478, 0.94904626, 0.95286433,
       0.95655439, 0.95987166, 0.96302521, 0.96608111, 0.96896657,
       0.97167277, 0.97434211, 0.9768161 , 0.97914747, 0.98140096,
       0.98356942, 0.98561757, 0.98759186, 0.98947807, 0.99131226,
       0.99307603, 0.99461855, 0.99606745, 0.99748678, 0.99883436,
       1.        ])
In [69]:
pca = PCA(n_components = 56) 
pca_tf = pca.fit_transform(azdias_scaled)

The first the most important components explains 12.39% of variance.

In [456]:
print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))

Explained variation per principal component: [0.12386682 0.10230773 0.0456946  0.03351101 0.0273043  0.02556035
 0.01965104 0.01726122 0.01681388 0.01645279 0.01548073 0.01422833
 0.01337998 0.01280474 0.01238682 0.01202275 0.01191729 0.01143873
 0.01107736 0.01065076 0.01043415 0.01040253 0.01019773 0.00990067
 0.0098285  0.00965548 0.00962477 0.00950021 0.00943025 0.00909144
 0.00899021 0.00875389 0.00861078 0.00852475 0.00847327 0.0083816
 0.00812169 0.00805042 0.00792343 0.00786337 0.00775031 0.0076857
 0.00756188 0.00753706 0.00742704 0.00735059 0.00728984 0.00723597
 0.00707062 0.00699227 0.00697598 0.00679873 0.00674426 0.00666869
 0.0065272  0.00648046]

I plotted variable and components chart to understand which columns are the most important for the component

In [71]:
plt.matshow(pca.components_,cmap ='viridis')
plt.yticks(np.arange(0, 50, 1.0),fontsize = 10)
plt.colorbar()
plt.xticks(range(len(azdias_upd_df.columns)),azdias_upd_df.columns,rotation=65,ha='left')
plt.tight_layout()
plt.show()

C:\Users\Ieva\Anaconda3\lib\site-packages\ipykernel_launcher.py:5: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  """

Apply PCA to customers dataset

In [458]:
pca_cs = pca.transform(customers_scaled)
1.2.1. analyze PCA

It's quite difficult to understand columns importance to the components while looking at the chart above. Then I wrote a function to show the most significant columns for the specific component.

In [73]:
def analyze_pca(pca, i, num):
    """Returns barplot of the most significant columns to the selected component

    Parameters:
        pca (model): fitted model;
        i (integer): nth components;
        num (integer): limit of columns

    Returns:
        visualization
    """       
    importance = {}
    for counter, feature in enumerate(azdias_upd_df.columns):
        importance[feature] = pca.components_[i][counter]
        
    sorted_by_values = dict(sorted(importance.items(), key = operator.itemgetter(1), reverse = True)[:num])
    keys = sorted_by_values.keys()
    values = sorted_by_values.values()
    plt.bar(keys, values)
    plt.xticks(rotation = 90)

PCA 1 indicates user's affinity and way of thinking: traditional, rational.

In [74]:
analyze_pca(pca, 1, 20)
In [462]:
# PCA 2 describes user's affinity and behavior related with marcantilism

# affinity indicating in what way the person is eventful orientated
# affinity indicating in what way the person is of a fightfull attitude
# shopping typology
# estimated household_net_income 
# typification of energy consumers
In [75]:
analyze_pca(pca, 2, 20)
In [463]:
# PCA 3: user's attitude and their transacting habits

# affinity indicating in what way the person is social minded
# estimated household_net_income 
# affinity indicating in what way the person is cultural minded
# Customer-Journey-Typology relating to the preferred information and buying channels of consumers
In [76]:
analyze_pca(pca, 3, 20)
In [464]:
# PCA4: Household activity in telko, bank and user's personal attitude. 

# transaction activity TELCO in the last 12 and 24 months (Household)
# transaction activity BANKS in the last 12 and 24 months
# affinity indicating in what way the person is of a fightfull attitude
In [77]:
analyze_pca(pca, 4, 20)
In [465]:
# PCA5: users financial activity and user's interest in bio/health products
# financial typology: main focus is the own house
# transactional activity based on the product group BIO FOOD PRODUCTS
# transactional activity based on the product group DIETARY SUPPLEMENTS
# transactional activity based on the product group COLLECTORS ITEMS
In [78]:
analyze_pca(pca, 5, 20)
1.3 Calculate number of clusters

For K-means algorithm it is important to define k (segments) value. As it is unsupervised algorithm, I should take additional steps to define k. This time I used Elbow method which evaluates the average distance from the center of the cluster.

In [469]:
def calculate_k(df, k_min, k_max, data):
    """ Return the elbow figure of the fitted kmeans models

    Parameters:
        df (dataframe): prepared dataframe for clustering
        k_min (integer): the minimum number of clusters;
        k_max (integer): the maximum number of clusters;

    Returns:
        visualization of elbow
    """   
    results = []
    K = range(k_min, k_max)
    for k in K:
        kmeanModel = KMeans(n_clusters = k, init = "k-means++")
        start = datetime.datetime.now()
        kmeanModel.fit(data)
        end = datetime.datetime.now()
        time_diff = end - start
        print("number of clusters {0}, calculation time {1} and inertia {2}".format(k, time_diff, kmeanModel.inertia_))
        results.append(kmeanModel.inertia_)
        
    plt.figure(figsize = (16,8))
    plt.plot(K, results, 'bx-', linestyle='--', marker='o')
    plt.xlabel('k')
    plt.ylabel('Distortion')
    plt.title('The Elbow Method showing the optimal k')
    plt.savefig('Elbow_plot_1_15.png')
    plt.show()
In [80]:
calculate_k(pca_tf, 1, 15, pca_tf)

number of clusters 1, calculation time 0:00:09.675120 and inertia 72695142.18536563
number of clusters 2, calculation time 0:00:53.199705 and inertia 65141492.936468326
number of clusters 3, calculation time 0:00:52.282186 and inertia 60328007.75929932
number of clusters 4, calculation time 0:01:57.312217 and inertia 57726655.375385076
number of clusters 5, calculation time 0:03:05.280385 and inertia 55968017.31678391
number of clusters 6, calculation time 0:02:09.452735 and inertia 54579157.56882607
number of clusters 7, calculation time 0:02:13.712388 and inertia 53486551.31572551
number of clusters 8, calculation time 0:02:31.658364 and inertia 52602550.87094875
number of clusters 9, calculation time 0:04:05.839432 and inertia 51537841.04643388
number of clusters 10, calculation time 0:03:18.309564 and inertia 50845235.09251847
number of clusters 11, calculation time 0:03:50.814622 and inertia 50012953.503860205
number of clusters 12, calculation time 0:07:51.947645 and inertia 49476775.85789525
number of clusters 13, calculation time 0:05:38.629313 and inertia 48951474.78051904
number of clusters 14, calculation time 0:06:30.754852 and inertia 48404984.20099927

I defined 8 clusters, as the difference between k=7 and k=8 decreased more than in previous steps

In [81]:
Image(filename = 'Elbow_plot_1_15.png')
Out[81]:
In [82]:
km = KMeans(
    n_clusters = 8, init='k-means++'
)
In [83]:
y_km = km.fit_predict(pca_tf)
In [85]:
y_cust = km.predict(pca_cs)
1.4. Compare segments of population and customer base

Number of clusters was selected on the population data. The same model was applied to customers database.

In [167]:
azdias_upd_df['population_cluster'] = y_km + 1
customers_upd_df['customer_cluster'] = y_cust + 1
In [168]:
azdias_upd_df['population_cluster'].value_counts()
Out[168]:
1    147339
5    145321
6    144462
7    108850
3    103842
8     97292
4     95824
2     48291
Name: population_cluster, dtype: int64
In [169]:
customers_upd_df['customer_cluster'].value_counts()
Out[169]:
4    46556
6    44144
1    29643
5    25725
7    20836
2    11761
3     7570
8     5417
Name: customer_cluster, dtype: int64
In [174]:
def plot_clusters(df, title):
    counts = df.value_counts(normalize = True, ascending = True)
    plt.figure(figsize = (10,5))
    sns.barplot(counts.index, counts.values, alpha = 0.8)
    plt.title(title)
    plt.ylabel('Number of Occurrences', fontsize=12)
    plt.xlabel('segment', fontsize=12)
    plt.show()
In [175]:
plot_clusters(customers_upd_df['customer_cluster'], "Customers base segments")
In [176]:
plot_clusters(azdias_upd_df['population_cluster'], "Population segments")

Overall, it seems that in the population, segments 1, 5, 6 have the highest share of people. There segment 2 is the least expressed. The same could be onserved in the customers'base. While in customers base, the biggest segment is 4 and the smallest of segment 8.

In [127]:
customers_upd_df.groupby('customer_cluster').describe().to_csv("customers_segments_stats.csv")
azdias_upd_df.groupby('population_cluster').describe().to_csv("customers_segments_stats.csv")
1.5 PCA importance to PCA
In [470]:
def segment_importance(km, segment_num, pca_len):
    """Returns the component importance to the segment

    Parameters:
        km (model): kmeans fitted model;
        segment_num (integer): specific segment;
        pca_len (integer): the number of columns used to create PCA

    Returns:
        dataframe: with components contribution to cluster
    """   
    weights =  km.cluster_centers_[segment_num]
    cluster_components = pd.DataFrame({"Contribution": weights, "Component": list(range(1, pca_len + 1))})
    res_df = cluster_components.sort_values("Contribution", ascending = False)
    return res_df
In [180]:
segment_importance(km, 0, 56)[0:10]
Out[180]:
Contribution Component
3 1.002314 4
2 0.766037 3
5 0.169627 6
33 0.155618 34
15 0.138790 16
4 0.107521 5
24 0.100889 25
16 0.086049 17
36 0.084077 37
39 0.064681 40
In [181]:
segment_importance(km, 1, 56)[0:10]
Out[181]:
Contribution Component
0 8.122003 1
5 1.762070 6
7 1.669895 8
9 0.876614 10
11 0.496790 12
6 0.263767 7
14 0.230028 15
10 0.184573 11
44 0.171851 45
13 0.171466 14
In [163]:
segment_importance(km, 2, 56)[0:10]
Out[163]:
Contribution Component
4 2.302590 5
5 1.360059 6
11 0.255753 12
18 0.225283 19
10 0.181514 11
17 0.168076 18
12 0.147664 13
14 0.091444 15
9 0.077041 10
31 0.075043 32
In [182]:
segment_importance(km, 4, 56)[0:10]
Out[182]:
Contribution Component
2 0.914755 3
7 0.477481 8
11 0.452097 12
9 0.317633 10
31 0.236039 32
18 0.204038 19
10 0.201413 11
30 0.190770 31
3 0.188201 4
17 0.182900 18
In [479]:
segment_importance(km, 5, 56)[0:10]
Out[479]:
Contribution Component
0 2.528325 1
8 0.306065 9
18 0.173529 19
30 0.128513 31
50 0.077822 51
49 0.075816 50
13 0.051626 14
38 0.038371 39
51 0.031491 52
29 0.025939 30
In [184]:
segment_importance(km, 7, 56)[0:10]
Out[184]:
Contribution Component
1 3.534462 2
3 1.694557 4
10 0.257733 11
49 0.135609 50
36 0.120268 37
29 0.105600 30
13 0.105195 14
20 0.097119 21
48 0.095962 49
53 0.077752 54

Did so far:

  1. Feature scaling to apply PCA
  2. PCA application, defined number of components.
  3. Described the first 5 components
  4. Applied K-Means clustering to data
  5. Selected number of segments
  6. Compared the population and customers base distributions.

Part 2: Supervised Learning Model

Now that you've found which parts of the population are more likely to be customers of the mail-order company, it's time to build a prediction model. Each of the rows in the "MAILOUT" data files represents an individual that was targeted for a mailout campaign. Ideally, we should be able to use the demographic information from each individual to decide whether or not it will be worth it to include that person in the campaign.

The "MAILOUT" data has been split into two approximately equal parts, each with almost 43 000 data rows. In this part, you can verify your model with the "TRAIN" partition, which includes a column, "RESPONSE", that states whether or not a person became a customer of the company following the campaign. In the next part, you'll need to create predictions on the "TEST" partition, where the "RESPONSE" column has been withheld.

In [187]:
mailout_train = pd.read_csv('Udacity_MAILOUT_052018_TRAIN.csv', sep=',')
mailout = mailout_train.drop(['Unnamed: 0'], axis = 1)

C:\Users\Ieva\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3058: DtypeWarning: Columns (19,20) have mixed types.Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

As in the previous part, there are a lot of columns with missing data.

In [189]:
(mailout_train.isnull().sum()/len(mailout_train)*100).sort_values(ascending = False)
Out[189]:
ALTER_KIND4               99.904567
ALTER_KIND3               99.594991
ALTER_KIND2               98.240305
ALTER_KIND1               95.372655
KK_KUNDENTYP              58.926493
                            ...    
D19_VERSI_ONLINE_DATUM     0.000000
D19_VERSICHERUNGEN         0.000000
D19_VOLLSORTIMENT          0.000000
D19_WEIN_FEINKOST          0.000000
Unnamed: 0                 0.000000
Length: 368, dtype: float64

The share of the response values shows that the sample is extremely imbalanced.

In [473]:
mailout.RESPONSE.value_counts()
Out[473]:
0    42430
1      532
Name: RESPONSE, dtype: int64
In [315]:
mailout_data = prepare_data(mailout, mailout, 0.15, 0.8, False)

replace unknown column values
find mixed column types
replace mixed columns to single type
removed column with missing data with more than 0.15 percent
collect missing columns names
keep relevant columns

train sample share of missing data after transformations

In [316]:
plot_missing_values_share(mailout_data, 10)

train sample before any cleaning

In [318]:
plot_missing_values_share(mailout, 10)
In [474]:
# define train and response values
train_df = mailout_data.drop(['RESPONSE'], axis = 1)
train_response = mailout_data['RESPONSE']

mailout_imputed = impute_missing_data(train_df)
In [375]:
def best_model_search(model, param_grid, X_train = mailout_imputed, y_train = train_response):
    """
    Fits a classifier to its training data and prints its ROC AUC score.
    
    Parameters:
    - model: selected and defined classifier
    - param_grid (dict): parameters to go through all possible combinations
    - X_train (DataFrame): training dataset without missing data
    - y_train (Series): response value
            
    Returns:
    - classifier: input classifier fitted to the training data
    """
    grid = GridSearchCV(estimator = model, param_grid = param_grid, scoring = 'roc_auc', cv = 3, verbose = 2)
    grid.fit(X_train, y_train)
    print(grid.best_score_, grid.best_params_)
    
    return grid.best_estimator_
Random forest
In [360]:
# Random forest
n_estimators = [50, 100, 150]
max_depth = [2, 4, 8, 16]
max_features = ['auto', 'sqrt']
bootstrap = [True, False]

random_grid_rf = {'n_estimators': n_estimators,
               'max_features': max_features,
               'max_depth': max_depth,
               'bootstrap': bootstrap}

rf = RandomForestClassifier(random_state = 42, verbose = 1)
In [361]:
best_model_search(rf, random_grid_rf)

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.5s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    1.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    1.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    1.6s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    1.6s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    1.6s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.8s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.9s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.5s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    2.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    2.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    2.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    3.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    3.8s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    3.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    2.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    2.3s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    2.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    3.3s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    3.9s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    3.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.8s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.8s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    3.3s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    3.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    5.8s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    5.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    5.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.8s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    3.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    3.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    5.8s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    4.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    5.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    6.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    6.3s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    7.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.3s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.5s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    6.9s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    6.9s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.3s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    6.6s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.8s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    1.6s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    1.9s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    2.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    2.5s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    2.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    2.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    1.6s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    1.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    1.6s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    2.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    2.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    2.5s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.6s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.4s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    6.6s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    6.6s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.3s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.9s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    5.9s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    9.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    9.3s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    3.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    6.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    6.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    6.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    8.9s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    9.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    9.3s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

0.6775335566613231 {'bootstrap': False, 'max_depth': 4, 'max_features': 'auto', 'n_estimators': 100}

[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    2.9s finished
Out[361]:
RandomForestClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=4, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=42, verbose=1,
                       warm_start=False)
In [364]:
# roc_auc 0.6775335566613231 
rf_best_values = RandomForestClassifier(random_state = 42, bootstrap = False, max_depth = 4, max_features = 'auto', n_estimators = 100, verbose = 1)
In [480]:
rf_base = best_model_search(rf, {})

Fitting 3 folds for each of 1 candidates, totalling 3 fits
[CV]  ................................................................

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.6s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

[CV] ................................................. , total=   5.2s
[CV]  ................................................................

[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.9s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

[CV] ................................................. , total=   5.4s
[CV]  ................................................................

[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   15.7s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

[CV] ................................................. , total=   5.2s
0.5825366205517647 {}

[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    7.7s finished
In [427]:
rf_best_values.fit(mailout_imputed, train_response)

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    5.0s finished
Out[427]:
RandomForestClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=4, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=42, verbose=1,
                       warm_start=False)
XgBoost
In [376]:
# XgBoost
n_estimators = [50, 100, 150]
max_depth = [2, 4, 6, 8, 16]
learning_rate = [0.01, 0.05, 0.1, 0.3]
reg_alpha = [1e-5, 1e-2, 0.1, 1, 100]

random_grid_rf = {'n_estimators': n_estimators,
               'max_depth': max_depth,
               'learning_rate': learning_rate, 
               'reg_alpha': reg_alpha}

xgb = XGBClassifier(random_state = 42)
xgb_find_value = best_model_search(xgb, random_grid_rf)

Fitting 3 folds for each of 300 candidates, totalling 900 fits
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=1e-05 

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=1e-05, total=   0.9s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=1e-05 

[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.8s remaining:    0.0s

[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=1e-05, total=   1.1s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=1e-05, total=   1.0s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=0.01, total=   1.0s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=0.01, total=   1.0s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=0.01, total=   1.2s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=0.1, total=   1.0s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=0.1, total=   1.0s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=0.1, total=   1.0s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=1, total=   1.1s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.01, max_depth=2, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=1e-05, total=   1.9s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=1e-05, total=   1.7s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=1e-05, total=   1.8s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=0.01, total=   1.7s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=0.01, total=   1.7s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=0.01, total=   1.7s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=0.1, total=   1.7s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=0.1, total=   1.6s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=0.1, total=   1.7s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=1, total=   1.6s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=1, total=   1.5s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=1, total=   1.4s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=100, total=   1.1s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=100, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=1e-05, total=   2.4s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=1e-05, total=   2.4s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=1e-05, total=   2.4s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=0.01, total=   2.8s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=0.01, total=   2.4s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=0.01, total=   2.4s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=0.1, total=   2.4s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=0.1, total=   2.4s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=0.1, total=   2.4s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=1, total=   2.2s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=1, total=   2.4s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=1, total=   2.1s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=100, total=   1.6s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=100, total=   2.1s
[CV] learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=2, n_estimators=150, reg_alpha=100, total=   1.9s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=1e-05, total=   1.3s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=1e-05, total=   1.2s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=1e-05, total=   1.3s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=0.01, total=   1.4s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=0.01, total=   1.4s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=0.01, total=   1.5s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=0.1, total=   1.3s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=0.1, total=   1.1s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=0.1, total=   1.3s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.01, max_depth=4, n_estimators=50, reg_alpha=100, total=   0.6s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=1e-05, total=   2.7s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=1e-05, total=   2.4s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=1e-05, total=   2.8s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=0.01, total=   2.7s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=0.01, total=   2.4s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=0.01, total=   2.6s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=0.1, total=   2.5s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=0.1, total=   2.3s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=0.1, total=   2.6s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=1, total=   1.9s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=1, total=   1.7s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=1, total=   1.8s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=100, total=   1.1s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=100, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=1e-05, total=   3.9s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=1e-05, total=   3.9s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=1e-05, total=   4.0s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=0.01, total=   3.9s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=0.01, total=   3.7s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=0.01, total=   4.1s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=0.1, total=   3.9s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=0.1, total=   3.6s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=0.1, total=   3.8s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=1, total=   3.1s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=1, total=   3.1s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=1, total=   3.1s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=100, total=   1.5s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=100, total=   2.0s
[CV] learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=4, n_estimators=150, reg_alpha=100, total=   1.9s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=1e-05, total=   1.4s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=1e-05, total=   1.2s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=1e-05, total=   1.4s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=0.01, total=   1.5s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=0.01, total=   1.2s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=0.01, total=   1.4s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=0.1, total=   1.3s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=0.1, total=   1.1s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=0.1, total=   1.2s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=100, total=   0.6s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.01, max_depth=6, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=1e-05, total=   3.0s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=1e-05, total=   2.8s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=1e-05, total=   3.0s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=0.01, total=   3.0s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=0.01, total=   2.7s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=0.01, total=   2.9s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=0.1, total=   2.9s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=0.1, total=   2.8s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=0.1, total=   2.8s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=1, total=   1.9s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=1, total=   1.7s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=1, total=   1.7s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=100, total=   1.1s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=100, reg_alpha=100, total=   1.2s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=1e-05, total=   5.0s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=1e-05, total=   4.6s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=1e-05, total=   4.9s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=0.01, total=   4.9s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=0.01, total=   4.5s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=0.01, total=   4.8s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=0.1, total=   4.7s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=0.1, total=   5.4s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=0.1, total=   5.2s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=1, total=   3.4s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=1, total=   3.2s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=1, total=   3.3s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=100, total=   1.6s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=100, total=   2.0s
[CV] learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=6, n_estimators=150, reg_alpha=100, total=   1.9s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=1e-05, total=   1.4s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=1e-05, total=   1.2s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=1e-05, total=   1.4s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=0.01, total=   1.4s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=0.01, total=   1.2s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=0.01, total=   1.5s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=0.1, total=   1.3s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=0.1, total=   1.1s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=0.1, total=   1.3s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.01, max_depth=8, n_estimators=50, reg_alpha=100, total=   0.6s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=1e-05, total=   3.3s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=1e-05, total=   2.8s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=1e-05, total=   3.5s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=0.01, total=   3.4s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=0.01, total=   2.8s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=0.01, total=   3.2s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=0.1, total=   3.1s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=0.1, total=   2.7s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=0.1, total=   3.0s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=1, total=   1.9s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=1, total=   1.7s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=1, total=   1.8s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=100, total=   1.1s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=100, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=1e-05, total=   5.7s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=1e-05, total=   5.3s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=1e-05, total=   5.8s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=0.01, total=   5.7s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=0.01, total=   5.0s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=0.01, total=   5.2s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=0.1, total=   5.5s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=0.1, total=   4.7s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=0.1, total=   5.0s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=1, total=   3.8s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=1, total=   3.0s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=1, total=   3.4s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=100, total=   1.6s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=100, total=   2.0s
[CV] learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=8, n_estimators=150, reg_alpha=100, total=   1.9s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=1e-05, total=   1.5s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=1e-05, total=   1.4s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=1e-05, total=   1.5s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=0.01, total=   1.5s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=0.01, total=   1.2s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=0.01, total=   1.4s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=0.1, total=   1.4s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=0.1, total=   1.1s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=0.1, total=   1.3s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=1, total=   0.8s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=1 ..
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=1, total=   0.7s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=50, reg_alpha=100, total=   0.8s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=1e-05, total=   4.4s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=1e-05, total=   3.0s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=1e-05, total=   3.8s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=0.01, total=   3.9s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=0.01, total=   3.1s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=0.01, total=   3.8s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=0.1, total=   3.7s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=0.1, total=   2.8s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=0.1, total=   3.4s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=1 .
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=1, total=   2.1s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=1 .
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=1, total=   1.8s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=1 .
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=1, total=   1.8s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=100, total=   1.1s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=100, reg_alpha=100, total=   1.2s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=1e-05, total=   7.1s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=1e-05, total=   6.5s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=1e-05, total=   6.7s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=0.01, total=   7.2s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=0.01, total=   6.3s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=0.01, total=   6.9s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=0.1, total=   6.6s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=0.1, total=   5.5s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=0.1, total=   6.4s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=1 .
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=1, total=   4.1s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=1 .
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=1, total=   3.0s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=1 .
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=1, total=   3.6s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=100, total=   1.6s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=100, total=   2.3s
[CV] learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.01, max_depth=16, n_estimators=150, reg_alpha=100, total=   2.0s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=1e-05, total=   1.1s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=1e-05, total=   1.1s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=1e-05, total=   1.0s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=0.01, total=   0.9s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=0.01, total=   0.9s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=0.01, total=   0.9s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=0.1, total=   0.9s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=0.1, total=   0.9s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=0.1, total=   0.9s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=1, total=   0.9s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=1, total=   0.9s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=1, total=   0.9s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=100, total=   1.0s
[CV] learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.05, max_depth=2, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=1e-05, total=   1.7s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=1e-05, total=   1.7s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=1e-05, total=   1.7s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=0.01, total=   1.7s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=0.01, total=   1.7s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=0.01, total=   1.7s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=0.1, total=   1.7s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=0.1, total=   1.7s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=0.1, total=   1.9s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=1, total=   1.7s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=1, total=   1.6s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=1, total=   2.0s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=100, total=   1.7s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=100, total=   2.0s
[CV] learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=100, reg_alpha=100, total=   1.9s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=1e-05, total=   2.8s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=1e-05, total=   2.6s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=1e-05, total=   2.4s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=0.01, total=   2.4s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=0.01, total=   2.4s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=0.01, total=   2.4s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=0.1, total=   2.4s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=0.1, total=   2.5s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=0.1, total=   2.5s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=1, total=   2.4s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=1, total=   2.4s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=1, total=   2.3s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=100, total=   2.1s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=100, total=   2.3s
[CV] learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=2, n_estimators=150, reg_alpha=100, total=   2.5s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=1e-05, total=   1.6s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=1e-05, total=   1.4s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=1e-05, total=   1.5s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=0.01, total=   1.6s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=0.01, total=   1.4s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=0.01, total=   1.5s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=0.1, total=   1.4s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=0.1, total=   1.4s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=0.1, total=   1.4s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=1, total=   1.3s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=1, total=   1.4s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=1, total=   1.3s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.05, max_depth=4, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=1e-05, total=   2.8s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=1e-05, total=   2.7s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=1e-05, total=   2.8s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=0.01, total=   2.8s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=0.01, total=   2.9s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=0.01, total=   2.8s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=0.1, total=   2.8s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=0.1, total=   2.7s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=0.1, total=   2.8s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=1, total=   2.7s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=1, total=   2.6s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=1, total=   2.6s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=100, total=   1.5s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=100, total=   2.1s
[CV] learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=100, reg_alpha=100, total=   2.2s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=1e-05, total=   4.1s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=1e-05, total=   4.3s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=1e-05, total=   4.1s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=0.01, total=   4.1s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=0.01, total=   4.1s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=0.01, total=   4.2s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=0.1, total=   4.1s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=0.1, total=   4.0s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=0.1, total=   4.7s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=1, total=   4.0s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=1, total=   3.9s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=1, total=   3.9s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=100, total=   2.8s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=100, total=   3.5s
[CV] learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=4, n_estimators=150, reg_alpha=100, total=   3.2s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=1e-05, total=   1.9s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=1e-05, total=   1.9s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=1e-05, total=   1.9s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=0.01, total=   1.9s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=0.01, total=   1.8s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=0.01, total=   2.0s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=0.1, total=   1.8s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=0.1, total=   1.8s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=0.1, total=   1.8s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=1, total=   1.6s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=1, total=   1.5s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=1, total=   1.5s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.05, max_depth=6, n_estimators=50, reg_alpha=100, total=   0.8s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=1e-05, total=   4.0s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=1e-05, total=   3.8s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=1e-05, total=   3.8s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=0.01, total=   3.8s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=0.01, total=   3.9s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=0.01, total=   3.8s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=0.1, total=   3.8s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=0.1, total=   3.7s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=0.1, total=   3.9s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=1, total=   3.5s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=1, total=   3.4s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=1, total=   3.4s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=100, total=   1.6s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=100, total=   2.2s
[CV] learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=100, reg_alpha=100, total=   2.3s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=1e-05, total=   5.7s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=1e-05, total=   5.7s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=1e-05, total=   5.9s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=0.01, total=   5.8s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=0.01, total=   5.6s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=0.01, total=   5.9s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=0.1, total=   5.7s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=0.1, total=   5.6s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=0.1, total=   6.0s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=1, total=   5.4s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=1, total=   5.3s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=1, total=   5.5s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=100, total=   3.0s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=100, total=   4.2s
[CV] learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=6, n_estimators=150, reg_alpha=100, total=   4.4s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=1e-05, total=   2.7s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=1e-05, total=   2.2s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=1e-05, total=   2.2s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=0.01, total=   2.2s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=0.01, total=   2.1s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=0.01, total=   2.2s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=0.1, total=   2.3s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=0.1, total=   2.1s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=0.1, total=   2.1s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=1, total=   1.8s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=1, total=   1.6s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=1, total=   1.7s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=100, total=   0.7s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.05, max_depth=8, n_estimators=50, reg_alpha=100, total=   0.8s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=1e-05, total=   5.0s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=1e-05, total=   4.7s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=1e-05, total=   4.8s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=0.01, total=   4.8s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=0.01, total=   4.9s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=0.01, total=   4.8s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=0.1, total=   4.8s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=0.1, total=   5.0s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=0.1, total=   4.7s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=1, total=   4.3s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=1, total=   4.3s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=1, total=   4.3s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=100, total=   1.5s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=100, total=   2.2s
[CV] learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=100, reg_alpha=100, total=   2.3s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=1e-05, total=   8.5s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=1e-05, total=   7.3s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=1e-05, total=   7.5s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=0.01, total=   7.3s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=0.01, total=   7.6s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=0.01, total=   7.2s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=0.1, total=   7.3s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=0.1, total=   7.3s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=0.1, total=   7.4s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=1, total=   7.0s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=1, total=   6.7s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=1, total=   7.0s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=100, total=   3.0s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=100, total=   4.0s
[CV] learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=8, n_estimators=150, reg_alpha=100, total=   3.6s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=1e-05, total=   3.3s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=1e-05, total=   3.2s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=1e-05, total=   3.2s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=0.01, total=   3.1s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=0.01, total=   3.0s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=0.01, total=   3.2s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=0.1, total=   3.2s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=0.1, total=   2.8s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=0.1, total=   2.9s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=1, total=   2.2s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=1, total=   1.9s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=1 ..
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=1, total=   2.1s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=100, total=   0.8s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=1e-05, total=   7.6s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=1e-05, total=   7.8s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=1e-05, total=   7.5s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=0.01, total=   7.6s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=0.01, total=   7.6s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=0.01, total=   7.6s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=0.1, total=   7.4s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=0.1, total=   7.4s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=0.1, total=   7.6s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=1 .
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=1, total=   6.3s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=1 .
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=1, total=   7.1s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=1 .
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=1, total=   7.1s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=100, total=   1.6s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=100, total=   2.2s
[CV] learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=100, reg_alpha=100, total=   2.3s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=1e-05, total=  12.2s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=1e-05, total=  12.3s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=1e-05, total=  12.4s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=0.01, total=  13.0s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=0.01, total=  12.1s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=0.01, total=  12.3s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=0.1, total=  12.1s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=0.1, total=  12.1s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=0.1, total=  11.9s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=1 .
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=1, total=  10.9s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=1 .
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=1, total=  11.1s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=1 .
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=1, total=  10.8s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=100, total=   3.0s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=100, total=   4.0s
[CV] learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.05, max_depth=16, n_estimators=150, reg_alpha=100, total=   3.6s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=1e-05, total=   0.9s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=1e-05, total=   0.9s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=1e-05, total=   0.9s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=0.01, total=   0.9s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=0.01, total=   1.0s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=0.01, total=   1.0s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=0.1, total=   1.0s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=0.1, total=   0.9s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=0.1, total=   0.9s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=1, total=   0.9s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=1, total=   0.9s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=1, total=   0.9s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=100, total=   0.8s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.1, max_depth=2, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=1e-05, total=   1.7s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=1e-05, total=   1.7s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=1e-05, total=   1.7s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=0.01, total=   1.7s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=0.01, total=   1.8s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=0.01, total=   1.7s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=0.1, total=   1.7s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=0.1, total=   1.7s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=0.1, total=   1.7s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=1, total=   1.7s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=1, total=   1.7s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=1, total=   1.6s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=100, total=   1.5s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=100, total=   1.8s
[CV] learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=100, reg_alpha=100, total=   1.7s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=1e-05, total=   2.4s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=1e-05, total=   2.4s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=1e-05, total=   2.4s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=0.01, total=   2.4s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=0.01, total=   2.4s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=0.01, total=   2.6s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=0.1, total=   2.5s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=0.1, total=   2.4s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=0.1, total=   2.4s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=1, total=   2.4s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=1, total=   2.4s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=1, total=   2.4s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=100, total=   2.3s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=100, total=   2.1s
[CV] learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=2, n_estimators=150, reg_alpha=100, total=   2.2s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=1e-05, total=   1.5s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=1e-05, total=   1.5s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=1e-05, total=   1.5s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=0.01, total=   1.5s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=0.01, total=   1.5s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=0.01, total=   1.5s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=0.1, total=   1.6s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=0.1, total=   1.5s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=0.1, total=   1.5s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=1, total=   1.4s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=1, total=   1.4s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=1, total=   1.4s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=100, total=   1.2s
[CV] learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.1, max_depth=4, n_estimators=50, reg_alpha=100, total=   1.1s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=1e-05, total=   2.8s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=1e-05, total=   2.9s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=1e-05, total=   2.8s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=0.01, total=   2.8s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=0.01, total=   2.8s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=0.01, total=   2.8s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=0.1, total=   2.8s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=0.1, total=   3.0s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=0.1, total=   2.8s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=1, total=   2.7s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=1, total=   2.7s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=1, total=   2.7s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=100, total=   2.0s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=100, total=   2.6s
[CV] learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=100, reg_alpha=100, total=   2.4s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=1e-05, total=   4.7s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=1e-05, total=   4.9s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=1e-05, total=   4.4s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=0.01, total=   4.1s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=0.01, total=   4.1s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=0.01, total=   4.1s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=0.1, total=   4.4s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=0.1, total=   4.1s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=0.1, total=   4.1s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=1, total=   4.0s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=1, total=   4.2s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=1, total=   4.1s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=100, total=   2.8s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=100, total=   2.9s
[CV] learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=4, n_estimators=150, reg_alpha=100, total=   2.9s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=1e-05, total=   2.1s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=1e-05, total=   2.0s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=1e-05, total=   2.0s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=0.01, total=   2.0s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=0.01, total=   2.0s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=0.01, total=   2.0s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=0.1, total=   2.0s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=0.1, total=   2.0s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=0.1, total=   2.2s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=1, total=   1.8s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=1, total=   1.8s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=1, total=   1.8s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=100, total=   1.2s
[CV] learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.1, max_depth=6, n_estimators=50, reg_alpha=100, total=   1.2s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=1e-05, total=   3.9s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=1e-05, total=   4.1s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=1e-05, total=   3.9s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=0.01, total=   3.9s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=0.01, total=   3.9s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=0.01, total=   4.1s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=0.1, total=   4.0s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=0.1, total=   4.2s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=0.1, total=   3.8s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=1, total=   4.0s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=1, total=   3.7s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=1, total=   3.9s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=100, total=   2.2s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=100, total=   2.7s
[CV] learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=100, reg_alpha=100, total=   2.9s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=1e-05, total=   5.8s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=1e-05, total=   5.7s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=1e-05, total=   6.0s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=0.01, total=   5.8s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=0.01, total=   5.8s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=0.01, total=   6.0s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=0.1, total=   5.7s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=0.1, total=   5.9s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=0.1, total=   5.7s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=1, total=   5.8s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=1, total=   5.8s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=1, total=   5.6s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=100, total=   3.0s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=100, total=   3.2s
[CV] learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=6, n_estimators=150, reg_alpha=100, total=   3.4s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=1e-05, total=   2.5s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=1e-05, total=   2.4s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=1e-05, total=   2.5s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=0.01, total=   2.5s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=0.01, total=   2.5s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=0.01, total=   2.6s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=0.1, total=   2.5s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=0.1, total=   2.4s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=0.1, total=   2.4s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=1, total=   2.2s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=1, total=   2.2s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=1, total=   2.2s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=100, total=   1.4s
[CV] learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.1, max_depth=8, n_estimators=50, reg_alpha=100, total=   1.2s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=1e-05, total=   5.0s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=1e-05, total=   4.9s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=1e-05, total=   5.1s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=0.01, total=   5.0s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=0.01, total=   5.0s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=0.01, total=   5.0s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=0.1, total=   5.1s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=0.1, total=   4.9s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=0.1, total=   4.9s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=1, total=   5.0s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=1, total=   4.7s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=1, total=   5.3s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=100, total=   2.8s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=100, total=   3.5s
[CV] learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=100, reg_alpha=100, total=   2.7s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=1e-05, total=   7.4s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=1e-05, total=   7.9s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=1e-05, total=   8.9s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=0.01, total=   8.8s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=0.01, total=   8.1s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=0.01, total=   8.2s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=0.1, total=   8.2s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=0.1, total=   8.2s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=0.1, total=  10.3s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=1, total=   9.7s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=1, total=   8.3s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=1, total=   7.7s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=100, total=   3.1s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=100, total=   3.6s
[CV] learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=8, n_estimators=150, reg_alpha=100, total=   3.2s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=1e-05, total=   4.1s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=1e-05, total=   4.7s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=1e-05, total=   4.1s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=0.01, total=   4.1s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=0.01, total=   3.9s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=0.01, total=   4.4s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=0.1, total=   4.3s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=0.1, total=   3.8s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=0.1, total=   3.7s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=1, total=   3.7s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=1, total=   3.7s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=1, total=   4.0s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.1, max_depth=16, n_estimators=50, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=1e-05, total=   9.6s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=1e-05, total=   9.3s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=1e-05, total=   9.2s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=0.01, total=   8.5s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=0.01, total=   8.7s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=0.01, total=   8.5s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=0.1, total=   8.3s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=0.1, total=   8.5s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=0.1, total=   9.2s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=1, total=   8.4s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=1, total=   8.5s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=1, total=   8.7s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=100, total=   2.6s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=100, total=   3.2s
[CV] learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=100, reg_alpha=100, total=   3.0s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=1e-05, total=  13.9s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=1e-05, total=  15.2s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=1e-05, total=  13.6s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=0.01, total=  14.0s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=0.01, total=  12.9s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=0.01, total=  14.2s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=0.1, total=  13.3s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=0.1, total=  12.9s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=0.1, total=  12.8s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=1, total=  12.3s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=1, total=  11.7s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=1, total=  12.2s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=100, total=   3.0s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=100, total=   3.6s
[CV] learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.1, max_depth=16, n_estimators=150, reg_alpha=100, total=   3.5s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=1e-05, total=   1.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=1e-05, total=   1.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=1e-05, total=   0.9s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=0.01, total=   1.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=0.01, total=   1.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=0.01, total=   1.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=0.1, total=   1.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=0.1, total=   1.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=0.1, total=   1.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=1, total=   1.1s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=1, total=   1.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=1, total=   1.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=100, total=   0.8s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=100, total=   0.8s
[CV] learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.3, max_depth=2, n_estimators=50, reg_alpha=100, total=   0.9s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=1e-05, total=   1.8s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=1e-05, total=   1.8s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=1e-05, total=   2.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=0.01, total=   1.9s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=0.01, total=   1.9s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=0.01, total=   1.9s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=0.1, total=   1.9s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=0.1, total=   1.7s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=0.1, total=   1.8s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=1, total=   1.8s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=1, total=   1.8s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=1, total=   1.9s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=100, total=   1.5s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=100, total=   1.4s
[CV] learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=100, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=1e-05, total=   2.9s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=1e-05, total=   2.7s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=1e-05, total=   2.7s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=0.01, total=   2.7s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=0.01, total=   2.6s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=0.01, total=   2.4s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=0.1, total=   3.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=0.1, total=   2.8s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=0.1, total=   2.7s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=1, total=   2.6s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=1, total=   2.6s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=1, total=   2.5s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=100, total=   2.0s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=100, total=   1.7s
[CV] learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=2, n_estimators=150, reg_alpha=100, total=   1.7s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=1e-05, total=   1.5s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=1e-05, total=   1.5s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=1e-05, total=   1.5s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=0.01, total=   1.5s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=0.01, total=   1.5s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=0.01, total=   1.5s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=0.1, total=   1.5s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=0.1, total=   1.6s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=0.1, total=   1.6s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=1, total=   1.5s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=1, total=   1.5s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=1, total=   1.5s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=100, total=   1.0s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=100, total=   1.0s
[CV] learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.3, max_depth=4, n_estimators=50, reg_alpha=100, total=   1.1s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=1e-05, total=   2.8s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=1e-05, total=   2.8s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=1e-05, total=   3.0s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=0.01, total=   2.9s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=0.01, total=   2.8s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=0.01, total=   2.8s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=0.1, total=   2.8s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=0.1, total=   3.0s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=0.1, total=   2.9s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=1, total=   2.8s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=1, total=   2.8s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=1, total=   2.8s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=100, total=   1.5s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=100, total=   1.5s
[CV] learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=100, reg_alpha=100, total=   1.7s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=1e-05, total=   4.1s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=1e-05, total=   4.2s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=1e-05, total=   4.2s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=0.01, total=   4.3s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=0.01, total=   4.1s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=0.01, total=   4.9s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=0.1, total=   5.1s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=0.1, total=   4.3s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=0.1, total=   4.1s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=1, total=   4.1s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=1, total=   4.3s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=1, total=   4.1s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=100, total=   1.9s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=100, total=   1.9s
[CV] learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=4, n_estimators=150, reg_alpha=100, total=   2.2s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=1e-05, total=   2.1s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=1e-05, total=   2.3s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=1e-05, total=   2.7s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=0.01, total=   2.1s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=0.01, total=   2.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=0.01, total=   2.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=0.1, total=   2.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=0.1, total=   2.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=0.1, total=   2.1s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=1, total=   2.2s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=1, total=   2.1s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=1, total=   2.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=100, total=   1.1s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=100, total=   1.1s
[CV] learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.3, max_depth=6, n_estimators=50, reg_alpha=100, total=   1.1s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=1e-05, total=   4.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=1e-05, total=   4.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=1e-05, total=   4.1s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=0.01, total=   4.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=0.01, total=   4.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=0.01, total=   4.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=0.1, total=   4.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=0.1, total=   3.9s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=0.1, total=   3.9s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=1, total=   4.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=1, total=   4.1s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=1, total=   3.9s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=100, total=   1.6s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=100, total=   1.6s
[CV] learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=100, reg_alpha=100, total=   1.6s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=1e-05, total=   6.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=1e-05, total=   5.9s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=1e-05, total=   5.8s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=0.01, total=   6.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=0.01, total=   5.8s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=0.01, total=   5.9s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=0.1, total=   5.9s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=0.1, total=   5.9s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=0.1, total=   6.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=1, total=   5.9s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=1, total=   5.8s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=1, total=   6.1s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=100, total=   2.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=100, total=   2.0s
[CV] learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=6, n_estimators=150, reg_alpha=100, total=   2.0s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=1e-05, total=   2.6s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=1e-05, total=   2.6s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=1e-05, total=   2.8s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=0.01, total=   2.7s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=0.01, total=   2.6s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=0.01 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=0.01, total=   2.6s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=0.1, total=   2.7s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=0.1, total=   2.6s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=0.1 ..
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=0.1, total=   2.8s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=1, total=   2.6s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=1, total=   2.5s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=1 ....
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=1, total=   2.7s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=100, total=   1.2s
[CV] learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=100 ..
[CV]  learning_rate=0.3, max_depth=8, n_estimators=50, reg_alpha=100, total=   1.1s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=1e-05, total=   5.3s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=1e-05, total=   5.1s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=1e-05, total=   5.1s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=0.01, total=   5.3s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=0.01, total=   5.1s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=0.01, total=   5.1s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=0.1, total=   5.3s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=0.1, total=   5.1s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=0.1, total=   5.1s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=1, total=   5.3s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=1, total=   5.1s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=1, total=   5.2s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=100, total=   1.6s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=100, total=   1.7s
[CV] learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=100, reg_alpha=100, total=   1.8s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=1e-05, total=   7.5s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=1e-05, total=   8.3s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=1e-05, total=   8.4s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=0.01, total=   7.8s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=0.01, total=   7.8s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=0.01, total=   7.5s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=0.1, total=   7.8s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=0.1, total=   7.5s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=0.1, total=   7.7s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=1, total=   7.5s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=1, total=   7.7s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=1, total=   8.0s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=100, total=   2.7s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=100, total=   2.7s
[CV] learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=8, n_estimators=150, reg_alpha=100, total=   2.5s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=1e-05, total=   5.0s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=1e-05, total=   4.9s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=1e-05, total=   4.8s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=0.01, total=   4.5s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=0.01, total=   4.7s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=0.01, total=   4.7s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=0.1, total=   4.3s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=0.1, total=   4.5s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=0.1 .
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=0.1, total=   4.3s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=1, total=   4.9s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=1, total=   4.4s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=1 ...
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=1, total=   3.9s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=100, total=   1.1s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=100, total=   1.2s
[CV] learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=100 .
[CV]  learning_rate=0.3, max_depth=16, n_estimators=50, reg_alpha=100, total=   1.3s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=1e-05, total=   8.7s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=1e-05, total=   8.8s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=1e-05, total=   8.6s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=0.01, total=   9.5s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=0.01, total=   9.5s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=0.01, total=  10.0s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=0.1, total=  10.0s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=0.1, total=   9.3s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=0.1 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=0.1, total=   9.2s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=1, total=   8.1s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=1, total=   7.7s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=1 ..
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=1, total=   7.4s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=100, total=   1.6s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=100, total=   1.6s
[CV] learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=100 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=100, reg_alpha=100, total=   1.8s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=1e-05, total=  13.2s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=1e-05, total=  12.9s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=1e-05 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=1e-05, total=  13.4s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=0.01, total=  14.5s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=0.01, total=  13.7s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=0.01 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=0.01, total=  12.8s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=0.1, total=  13.1s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=0.1, total=  13.5s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=0.1 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=0.1, total=  14.0s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=1, total=  13.1s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=1, total=  12.2s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=1 ..
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=1, total=  10.8s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=100, total=   2.1s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=100, total=   2.5s
[CV] learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=100 
[CV]  learning_rate=0.3, max_depth=16, n_estimators=150, reg_alpha=100, total=   2.3s

[Parallel(n_jobs=1)]: Done 900 out of 900 | elapsed: 52.6min finished

0.6982368437941732 {'learning_rate': 0.05, 'max_depth': 2, 'n_estimators': 100, 'reg_alpha': 0.01}
In [381]:
# XGBoost auc 0.6982368437941732 
xgb_best_values = XGBClassifier(random_state = 42, learning_rate = 0.05, max_depth = 2, n_estimators = 100, reg_alpha = 0.01)
In [382]:
# XGBoost with default values 0.5886985090458787 
xgb_find_base_value = best_model_search(xgb, {})

Fitting 3 folds for each of 1 candidates, totalling 3 fits
[CV]  ................................................................

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

[CV] ................................................. , total=   4.8s
[CV]  ................................................................

[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.7s remaining:    0.0s

[CV] ................................................. , total=   4.1s
[CV]  ................................................................
[CV] ................................................. , total=   4.2s

[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   13.0s finished

0.5886985090458787 {}
CatBoost
In [389]:
# CatBoost
max_depth = [2, 3, 6, 9, 10]
iterations = [100, 500]
learning_rate = [0.01, 0.1, 0.3]
l2_leaf_reg = [1, 10, 100]

random_grid_cb = {'iterations': iterations,
               'max_depth': max_depth,
               'learning_rate': learning_rate, 
               'l2_leaf_reg': l2_leaf_reg}

cbm = cb.CatBoostClassifier(random_state = 42)
cb_find_value = best_model_search(cbm, random_grid_cb)

Fitting 3 folds for each of 90 candidates, totalling 270 fits
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=2 ..

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=2, total=   2.0s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=2 ..

[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.9s remaining:    0.0s

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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=2, total=   1.9s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=2 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=2, total=   1.9s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=3 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=3, total=   2.1s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=3 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=3, total=   2.1s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=3 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=3, total=   2.0s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=6 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=6, total=   2.4s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=6 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=6, total=   3.0s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=6 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=6, total=   3.0s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=9 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=9, total=   4.5s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=9 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=9, total=   4.2s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=9 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=9, total=   3.7s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=10 .
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=10, total=   6.1s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=10 .
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=10, total=   7.0s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=10 .
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.01, max_depth=10, total=   7.2s
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=2, total=   1.6s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=2 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=2, total=   1.6s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=2 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=2, total=   1.7s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=3 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=3, total=   1.8s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=3 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=3, total=   1.9s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=3 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=3, total=   2.1s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=6 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=6, total=   2.7s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=6 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=6, total=   2.4s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=6 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=6, total=   3.0s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=9 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=9, total=   5.6s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=9 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=9, total=   5.5s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=9 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=9, total=   5.3s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=10 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=10, total=   7.5s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=10 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=10, total=   8.3s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=10 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.1, max_depth=10, total=   8.5s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=2 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=2, total=   2.2s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=2 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=2, total=   2.1s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=2 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=2, total=   2.5s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=3 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=3, total=   2.1s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=3 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=3, total=   2.1s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=3 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=3, total=   2.2s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=6 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=6, total=   2.7s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=6 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=6, total=   2.6s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=6 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=6, total=   2.4s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=9 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=9, total=   4.7s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=9 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=9, total=   4.0s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=9 ...
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=9, total=   4.3s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=10 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=10, total=   7.5s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=10 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=10, total=   8.0s
[CV] iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=10 ..
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[CV]  iterations=100, l2_leaf_reg=1, learning_rate=0.3, max_depth=10, total=   7.7s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=2 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=2, total=   2.1s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=2 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=2, total=   2.0s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=2 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=2, total=   1.9s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=3 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=3, total=   2.2s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=3 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=3, total=   1.9s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=3 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=3, total=   2.2s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=6 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=6, total=   2.2s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=6 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=6, total=   2.3s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=6 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=6, total=   2.5s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=9 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=9, total=   4.4s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=9 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=9, total=   4.3s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=9 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=9, total=   4.7s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=10 
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=10, total=   6.8s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=10 
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=10, total=   6.7s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=10 
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.01, max_depth=10, total=   6.6s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=2 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=2, total=   2.0s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=2 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=2, total=   2.2s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=2 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=2, total=   2.1s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=3 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=3, total=   1.8s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=3 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=3, total=   1.8s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=3 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=3, total=   1.8s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=6 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=6, total=   2.2s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=6 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=6, total=   2.2s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=6 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=6, total=   2.3s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=9 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=9, total=   3.6s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=9 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=9, total=   3.5s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=9 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=9, total=   3.5s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=10 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=10, total=   6.4s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=10 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=10, total=   6.1s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=10 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.1, max_depth=10, total=   6.6s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=2 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=2, total=   2.4s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=2 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=2, total=   2.5s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=2 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=2, total=   2.3s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=3 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=3, total=   2.1s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=3 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=3, total=   2.8s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=3 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=3, total=   2.9s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=6 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=6, total=   3.4s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=6 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=6, total=   3.0s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=6 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=6, total=   2.8s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=9 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=9, total=   4.7s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=9 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=9, total=   4.3s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=9 ..
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=9, total=   3.7s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=10 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=10, total=   7.4s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=10 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=10, total=   7.1s
[CV] iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=10 .
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[CV]  iterations=100, l2_leaf_reg=10, learning_rate=0.3, max_depth=10, total=   8.4s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=2 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=2, total=   2.0s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=2 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=2, total=   2.3s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=2 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=2, total=   2.5s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=3 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=3, total=   2.5s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=3 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=3, total=   2.0s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=3 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=3, total=   1.8s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=6 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=6, total=   2.2s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=6 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=6, total=   2.2s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=6 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=6, total=   2.2s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=9 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=9, total=   3.4s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=9 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=9, total=   3.4s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=9 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=9, total=   3.1s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=10 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=10, total=   5.0s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=10 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=10, total=   5.4s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=10 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.01, max_depth=10, total=   5.0s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=2 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=2, total=   1.9s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=2 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=2, total=   2.4s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=2 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=2, total=   2.3s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=3 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=3, total=   2.1s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=3 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=3, total=   2.7s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=3 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=3, total=   2.1s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=6 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=6, total=   2.4s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=6 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=6, total=   2.5s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=6 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=6, total=   2.5s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=9 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=9, total=   3.9s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=9 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=9, total=   4.0s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=9 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=9, total=   4.2s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=10 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=10, total=   6.9s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=10 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=10, total=   6.9s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=10 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.1, max_depth=10, total=   7.5s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=2 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=2, total=   1.9s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=2 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=2, total=   2.3s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=2 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=2, total=   2.0s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=3 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=3, total=   2.0s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=3 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=3, total=   2.3s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=3 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=3, total=   2.2s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=6 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=6, total=   2.5s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=6 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=6, total=   2.6s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=6 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=6, total=   2.5s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=9 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=9, total=   3.9s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=9 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=9, total=   4.3s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=9 .
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=9, total=   3.9s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=10 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=10, total=   6.9s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=10 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=10, total=   6.5s
[CV] iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=10 
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[CV]  iterations=100, l2_leaf_reg=100, learning_rate=0.3, max_depth=10, total=   6.7s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=2 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=2, total=   6.0s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=2 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=2, total=   6.2s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=2 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=2, total=   6.3s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=3 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=3, total=   6.9s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=3 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=3, total=   7.6s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=3 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=3, total=   7.2s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=6 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=6, total=   9.6s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=6 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=6, total=   9.0s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=6 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=6, total=   9.9s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=9 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=9, total=  17.1s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=9 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=9, total=  17.9s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=9 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=9, total=  20.2s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=10 .
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=10, total=  32.7s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=10 .
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=10, total=  31.0s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=10 .
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.01, max_depth=10, total=  32.0s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=2 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=2, total=   6.7s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=2 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=2, total=   6.3s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=2 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=2, total=   6.3s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=3 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=3, total=   7.6s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=3 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=3, total=   7.1s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=3 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=3, total=   7.0s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=6 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=6, total=   9.5s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=6 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=6, total=   9.4s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=6 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=6, total=  10.3s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=9 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=9, total=  17.5s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=9 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=9, total=  17.3s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=9 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=9, total=  17.8s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=10 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=10, total=  33.1s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=10 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=10, total=  31.5s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=10 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.1, max_depth=10, total=  33.4s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=2 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=2, total=   6.7s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=2 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=2, total=   6.2s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=2 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=2, total=   6.5s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=3 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=3, total=   7.7s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=3 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=3, total=   7.9s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=3 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=3, total=   7.4s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=6 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=6, total=   9.6s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=6 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=6, total=   9.7s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=6 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=6, total=   9.9s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=9 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=9, total=  16.5s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=9 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=9, total=  16.1s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=9 ...
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=9, total=  16.1s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=10 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=10, total=  30.9s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=10 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=10, total=  30.8s
[CV] iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=10 ..
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[CV]  iterations=500, l2_leaf_reg=1, learning_rate=0.3, max_depth=10, total=  31.4s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=2 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=2, total=   7.0s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=2 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=2, total=   7.3s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=2 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=2, total=   7.3s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=3 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=3, total=   7.5s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=3 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=3, total=   8.0s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=3 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=3, total=   7.6s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=6 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=6, total=   9.5s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=6 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=6, total=   9.2s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=6 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=6, total=   9.6s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=9 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=9, total=  19.1s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=9 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=9, total=  16.6s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=9 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=9, total=  16.2s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=10 
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=10, total=  31.8s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=10 
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=10, total=  30.0s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=10 
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.01, max_depth=10, total=  32.2s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=2 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=2, total=   6.7s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=2 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=2, total=   7.0s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=2 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=2, total=   7.3s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=3 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=3, total=   7.5s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=3 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=3, total=   7.9s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=3 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=3, total=   7.3s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=6 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=6, total=  10.2s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=6 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=6, total=  10.2s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=6 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=6, total=   9.8s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=9 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=9, total=  17.8s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=9 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=9, total=  16.8s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=9 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=9, total=  18.6s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=10 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=10, total=  32.9s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=10 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=10, total=  34.0s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=10 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.1, max_depth=10, total=  33.0s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=2 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=2, total=   7.6s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=2 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=2, total=   7.4s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=2 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=2, total=   7.0s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=3 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=3, total=   7.2s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=3 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=3, total=   7.5s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=3 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=3, total=   7.6s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=6 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=6, total=   9.8s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=6 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=6, total=   8.9s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=6 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=6, total=   9.4s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=9 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=9, total=  15.7s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=9 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=9, total=  15.3s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=9 ..
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=9, total=  15.7s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=10 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=10, total=  30.2s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=10 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=10, total=  34.7s
[CV] iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=10 .
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[CV]  iterations=500, l2_leaf_reg=10, learning_rate=0.3, max_depth=10, total=  32.5s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=2 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=2, total=   7.3s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=2 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=2, total=   7.5s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=2 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=2, total=   7.4s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=3 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=3, total=   8.1s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=3 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=3, total=   7.5s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=3 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=3, total=   8.8s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=6 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=6, total=   9.8s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=6 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=6, total=   9.2s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=6 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=6, total=   9.2s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=9 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=9, total=  15.7s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=9 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=9, total=  16.4s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=9 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=9, total=  16.1s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=10 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=10, total=  28.8s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=10 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=10, total=  30.1s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=10 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.01, max_depth=10, total=  28.9s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=2 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=2, total=   7.2s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=2 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=2, total=   6.9s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=2 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=2, total=   6.9s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=3 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=3, total=   7.4s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=3 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=3, total=   7.4s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=3 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=3, total=   7.5s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=6 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=6, total=   9.0s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=6 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=6, total=   8.6s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=6 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=6, total=   8.6s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=9 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=9, total=  16.5s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=9 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=9, total=  16.5s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=9 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=9, total=  16.0s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=10 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=10, total=  32.0s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=10 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=10, total=  31.7s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=10 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.1, max_depth=10, total=  31.3s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=2 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=2, total=   6.8s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=2 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=2, total=   6.8s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=2 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=2, total=   7.0s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=3 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=3, total=   7.2s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=3 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=3, total=   7.2s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=3 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=3, total=   7.2s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=6 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=6, total=   9.8s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=6 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=6, total=   8.8s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=6 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=6, total=   9.1s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=9 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=9, total=  16.4s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=9 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=9, total=  16.7s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=9 .
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=9, total=  17.6s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=10 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=10, total=  32.1s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=10 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=10, total=  31.4s
[CV] iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=10 
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[CV]  iterations=500, l2_leaf_reg=100, learning_rate=0.3, max_depth=10, total=  33.5s
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[Parallel(n_jobs=1)]: Done 270 out of 270 | elapsed: 40.7min finished

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0.695273513005537 {'iterations': 100, 'l2_leaf_reg': 10, 'learning_rate': 0.1, 'max_depth': 3}
In [420]:
# CatBoost auc 0.695273513005537  
cb_best_values = cb.CatBoostClassifier(random_state = 42, iterations = 100, l2_leaf_reg = 10, learning_rate = 0.1, max_depth = 3)
In [421]:
cb_best_values.fit(mailout_imputed, train_response)

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Out[421]:
<catboost.core.CatBoostClassifier at 0x19846f2b348>
In [396]:
# CatBoost Base 0.631340392011469 {}
cb_base_values = best_model_search(cbm, {})

Fitting 3 folds for each of 1 candidates, totalling 3 fits
[CV]  ................................................................

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

Learning rate set to 0.043159
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[CV] ................................................. , total=  18.9s
[CV]  ................................................................

[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   18.8s remaining:    0.0s

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[CV] ................................................. , total=  16.7s
[CV]  ................................................................
Learning rate set to 0.043159
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[CV] ................................................. , total=  16.6s
Learning rate set to 0.051317
0:	learn: 0.5899889	total: 18.2ms	remaining: 18.2s
1:	learn: 0.5032126	total: 36.4ms	remaining: 18.1s
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[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   52.2s finished

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0.631340392011469 {}
In [404]:
oversample = SMOTE()
In [406]:
train_df_smote, train_response_smote = oversample.fit_resample(mailout_imputed, train_response)
In [409]:
print(Counter(train_response_smote))

Counter({0: 42430, 1: 42430})
In [412]:
cb_best_values_SMOTE = best_model_search(cbm, {}, train_df_smote, train_response_smote)

Fitting 3 folds for each of 1 candidates, totalling 3 fits
[CV]  ................................................................

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

Learning rate set to 0.057716
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[CV] ................................................. , total=  46.5s
[CV]  ................................................................

[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   46.4s remaining:    0.0s

Learning rate set to 0.057716
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[CV] ................................................. , total=  46.4s
[CV]  ................................................................
Learning rate set to 0.057717
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[CV] ................................................. , total=  48.5s

[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  2.4min finished

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0.995647713958268 {}
In [445]:
smote_cb = GridSearchCV(estimator = cb.CatBoostClassifier(), param_grid = {}, scoring='roc_auc', cv = 3)
In [447]:
best_smote = smote_cb.fit(train_df_smote, train_response_smote)

Learning rate set to 0.057716
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In this section I:

  1. Applied data cleaning with the same rules as in the first section.
  2. Imputed missing data with imputer and median strategy
  3. Applied multiple models to find the best model. Firstly, tried base models without any tunning and with tunning. Models with extra effors works better than with default parameters.
  4. Compared results of Radom Forest, Ada Boost, CatBoost and selected as CatBoost the best one.
  5. Applied SMOTE to reduce the imbalance of the initial dataframe.

Part 3: Kaggle Competition

Now that you've created a model to predict which individuals are most likely to respond to a mailout campaign, it's time to test that model in competition through Kaggle. If you click on the link here, you'll be taken to the competition page where, if you have a Kaggle account, you can enter. If you're one of the top performers, you may have the chance to be contacted by a hiring manager from Arvato or Bertelsmann for an interview!

Your entry to the competition should be a CSV file with two columns. The first column should be a copy of "LNR", which acts as an ID number for each individual in the "TEST" partition. The second column, "RESPONSE", should be some measure of how likely each individual became a customer – this might not be a straightforward probability. As you should have found in Part 2, there is a large output class imbalance, where most individuals did not respond to the mailout. Thus, predicting individual classes and using accuracy does not seem to be an appropriate performance evaluation method. Instead, the competition will be using AUC to evaluate performance. The exact values of the "RESPONSE" column do not matter as much: only that the higher values try to capture as many of the actual customers as possible, early in the ROC curve sweep.

In [414]:
# mailout_test = pd.read_csv('../../data/Term2/capstone/arvato_data/Udacity_MAILOUT_052018_TEST.csv', sep=';')
mailout_test = pd.read_csv('Udacity_MAILOUT_052018_TEST.csv', sep = ',')
mailout_test = mailout_test.drop(['Unnamed: 0'], axis = 1)

C:\Users\Ieva\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3058: DtypeWarning: Columns (19,20) have mixed types.Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)
In [415]:
mailout_test_prep = prepare_data(mailout_test, mailout_test, 0.15, 0.8, False)

replace unknown column values
find mixed column types
replace mixed columns to single type
removed column with missing data with more than 0.15 percent
collect missing columns names
keep relevant columns
In [430]:
mailout_test_imputed = impute_missing_data(mailout_test_prep)
In [436]:
results = pd.DataFrame()
results['LNR'] = mailout_test['LNR']
results['RESPONSE'] = cb_best_values.predict_proba(mailout_test_imputed)[:, 1]
In [448]:
results = pd.DataFrame()
results['LNR'] = mailout_test['LNR']
results['RESPONSE'] = best_smote.predict_proba(mailout_test_imputed)[:, 1]
In [442]:
results.to_csv("kaggle_results_SMOTE.csv", index = False)
In [439]:
results
Out[439]:
LNR RESPONSE
0 1754 0.024315
1 1770 0.028167
2 1465 0.004852
3 1470 0.004533
4 1478 0.006857
... ... ...
42828 67615 0.011726
42829 67938 0.005512
42830 67942 0.010506
42831 67949 0.009519
42832 68898 0.014814

42833 rows × 2 columns

In [475]:
# Submitted results to kaggle
Image(filename = 'kaggle.png')
Out[475]:

Summary

To summarise, I take multiple steps to prepare data for modelling, dropped columns with high missing data share, checked correlations, mixed data types, identified unknown and undefined column values.

Then I applied PCA to reduce the data dimension even more after data preprocessing and spent some time to understand first components distribution. These components I used as an input for K-means clustering.

I selected K=1, .., 14 to define the best number of segments for population segments and then applied to the customers based, compared these segments. Customers and population segments have some similarities, but as expected more differences.

Finally, I created multiple models to predict if people will convert to customers after marketing campaign. For this step, I used CatBoost for the first time, and it was interesting to see the results.

Conclusions

This project let me to touch a real world Data Science project and problems.

Moreover, I understood how it is important to compare and understand not only the user base but also the potential of growth if you analyse the population. Also, how the big picture understanding, helps business.

I think it would be easier to work with this data if I had specific business knowledge and understanding. In this case, the number of columns was huge and it was quite difficult to understand the meaning.

I am sure, there are many more things to do related with this project. I did not used Pipelines as this solution is not going to be used in the production. At least what I would like to try is to apply ensemble method which combines multiple classification models.

To improve the model performance, it would be important to check the most important features, iterate with the lower number of features. Maybe the different down-sampling techniques would have helped to predict with higher accuracy. Lastly, I would like to thank Udacity for giving and teaching how to improve and solve real world business problems.